论文标题
布料操纵任务中的语义状态估计
Semantic State Estimation in Cloth Manipulation Tasks
论文作者
论文摘要
由于问题的复杂性和高维度，了解诸如纺织品之类的可变形物体操作（例如纺织品）是一个挑战。特别是，在连续的操作过程中，缺乏语义状态的通用表示（例如\ textit {crumpled}，\ textit {对角线折叠}）引入了识别操纵类型的障碍。在本文中，我们旨在解决布置操作任务中语义状态估计的问题。为此，我们介绍了一个新的大型全范围的RGB图像数据集，其中显示了各种人类对不同复杂布操作的演示。我们提供一组基线深网，并使用我们建议的数据集对语义状态估算问题进行基准测试。此外，我们研究了在机器人监视长而复杂的布料操作任务中我们语义状态估计框架的可伸缩性。
Understanding of deformable object manipulations such as textiles is a challenge due to the complexity and high dimensionality of the problem. Particularly, the lack of a generic representation of semantic states (e.g., \textit{crumpled}, \textit{diagonally folded}) during a continuous manipulation process introduces an obstacle to identify the manipulation type. In this paper, we aim to solve the problem of semantic state estimation in cloth manipulation tasks. For this purpose, we introduce a new large-scale fully-annotated RGB image dataset showing various human demonstrations of different complicated cloth manipulations. We provide a set of baseline deep networks and benchmark them on the problem of semantic state estimation using our proposed dataset. Furthermore, we investigate the scalability of our semantic state estimation framework in robot monitoring tasks of long and complex cloth manipulations.
